Evidential Communities for Complex Networks
arXiv:1501.01780 · doi:10.1007/978-3-319-08795-5_57
Abstract
Community detection is of great importance for understand-ing graph structure in social networks. The communities in real-world networks are often overlapped, i.e. some nodes may be a member of multiple clusters. How to uncover the overlapping communities/clusters in a complex network is a general problem in data mining of network data sets. In this paper, a novel algorithm to identify overlapping communi-ties in complex networks by a combination of an evidential modularity function, a spectral mapping method and evidential c-means clustering is devised. Experimental results indicate that this detection approach can take advantage of the theory of belief functions, and preforms good both at detecting community structure and determining the appropri-ate number of clusters. Moreover, the credal partition obtained by the proposed method could give us a deeper insight into the graph structure.
References in corpus (8)
- Community structure in social and biological networks
- Finding and evaluating community structure in networks
- Community detection in graphs
- Fast algorithm for detecting community structure in networks
- Uncovering the overlapping community structure of complex networks in nature and society
- Cooperative Game Theory Approaches for Network Partitioning
- Analyzing and Modeling Real-World Phenomena with Complex Networks: A Survey of Applications
- Fuzzy communities and the concept of bridgeness in complex networks